ABC random forests for Bayesian parameter inference

This preprint has been reviewed and recommended by Peer Community In Evolutionary Biology (http://dx.doi.org/10.24072/pci.evolbiol.100036). Approximate Bayesian computation (ABC) has grown into a standard methodology that manages Bayesian inference for models associated with intractable likelihood functions. Most ABC implementations require the preliminary selection of a vector of informative statistics summarizing raw data. Furthermore, in almost all existing implementations, the tolerance level that separates acceptance from rejection of simulated parameter values needs to be calibrated. We propose to conduct likelihood-free Bayesian inferences about parameters with no prior selection of the relevant components of the summary statistics and bypassing the derivation of the associated tolerance level. The approach relies on the random forest methodology of Breiman (2001) applied in a (non parametric) regression setting. We advocate the derivation of a new random forest for each component of the parameter vector of interest. When compared with earlier ABC solutions, this method offers significant gains in terms of robustness to the choice of the summary statistics, does not depend on any type of tolerance level, and is a good trade-off in term of quality of point estimator precision and credible interval estimations for a given computing time. We illustrate the performance of our methodological proposal and compare it with earlier ABC methods on a Normal toy example and a population genetics example dealing with human population evolution. All methods designed here have been incorporated in the R package abcrf (version 1.7) available on CRAN.Comment: Main text: 24 pages, 6 figures Supplementary Information: 14 pages, 5 figure

Cost-based feature selection for network model choice

Selecting a small set of informative features from a large number of possibly noisy candidates is a challenging problem with many applications in machine learning and approximate Bayesian computation. In practice, the cost of computing informative features also needs to be considered. This is particularly important for networks because the computational costs of individual features can span several orders of magnitude. We addressed this issue for the network model selection problem using two approaches. First, we adapted nine feature selection methods to account for the cost of features. We show for two classes of network models that the cost can be reduced by two orders of magnitude without considerably affecting classification accuracy (proportion of correctly identified models). Second, we selected features using pilot simulations with smaller networks. This approach reduced the computational cost by a factor of 50 without affecting classification accuracy. To demonstrate the utility of our approach, we applied it to three different yeast protein interaction networks and identified the best-fitting duplication divergence model.Comment: 34 pages, 6 figure

Towards an engineering approach for advanced interaction techniques in 3D environments

National audienceIn recent years, Virtual Environments have appeared in new areas such as mass-market, web or mobile situations. In parallel, advanced forms ofinteractions are emerging such as tactile, mixed, tangible or spatial user interfaces, promoting ease of learning and use. To contribute to the democratization of 3D Virtual Environments(3DVE) and their use by persons who are not experts in 3D and occasional users, simultaneously considering Computer Graphics and Human Computer Interaction design considerations is required. In this position paper, we first provide an overview of a new analytical framework for the design of advanced interaction techniques for 3D Virtual Environment. It consists in identifying links that support the interaction and connect user’s tasks to be performed in a 3DVE with the targeted scene graph. We relate our work to existing modeling approaches and discuss about our expectations with regards to the engineering of advanced interaction techniqu

Smartphone Based 3D Navigation Techniques in an Astronomical Observatory Context: Implementation and Evaluation in a Software Platform

International audience3D Virtual Environments (3DVE) come up as a good solution to transmit knowledge in a museum exhibit. In such contexts, providing easy to learn and to use interaction techniques which facilitate the handling inside a 3DVE is crucial to maximize the knowledge transfer. We took the opportunity to design and implement a software platform for explaining the behavior of the Telescope Bernard-Lyot to museum visitors on top of the Pic du Midi. Beyond the popularization of a complex scientific equipment, this platform constitutes an open software environment to easily plug different 3D interaction techniques. Recently, popular use of a smartphones as personal handled computer lets us envision the use of a mobile device as an interaction support with these 3DVE. Accordingly, we design and propose how to use the smartphone as a tangible object to navigate inside a 3DVE. In order to prove the interest in the use of smartphones, we compare our solution with available solutions: keyboard-mouse and 3D mouse. User experiments confirmed our hypothesis and particularly emphasizes that visitors find our solution more attractive and stimulating. Finally, we illustrate the benefits of our software framework by plugging alternative interaction techniques for supporting selection and manipulation task in 3D

Single-cell transcriptomics reveals shared immunosuppressive landscapes of mouse and human neuroblastoma

BACKGROUND High-risk neuroblastoma is a pediatric cancer with still a dismal prognosis, despite multimodal and intensive therapies. Tumor microenvironment represents a key component of the tumor ecosystem the complexity of which has to be accurately understood to define selective targeting opportunities, including immune-based therapies. METHODS We combined various approaches including single-cell transcriptomics to dissect the tumor microenvironment of both a transgenic mouse neuroblastoma model and a cohort of 10 biopsies from neuroblastoma patients, either at diagnosis or at relapse. Features of related cells were validated by multicolor flow cytometry and functional assays. RESULTS We show that the immune microenvironment of MYCN-driven mouse neuroblastoma is characterized by a low content of T cells, several phenotypes of macrophages and a population of cells expressing signatures of myeloid-derived suppressor cells (MDSCs) that are molecularly distinct from the various macrophage subsets. We document two cancer-associated fibroblasts (CAFs) subsets, one of which corresponding to CAF-S1, known to have immunosuppressive functions. Our data unravel a complex content in myeloid cells in patient tumors and further document a striking correspondence of the microenvironment populations between both mouse and human tumors. We show that mouse intratumor T cells exhibit increased expression of inhibitory receptors at the protein level. Consistently, T cells from patients are characterized by features of exhaustion, expressing inhibitory receptors and showing low expression of effector cytokines. We further functionally demonstrate that MDSCs isolated from mouse neuroblastoma have immunosuppressive properties, impairing the proliferation of T lymphocytes. CONCLUSIONS Our study demonstrates that neuroblastoma tumors have an immunocompromised microenvironment characterized by dysfunctional T cells and accumulation of immunosuppressive cells. Our work provides a new and precious data resource to better understand the neuroblastoma ecosystem and suggest novel therapeutic strategies, targeting both tumor cells and components of the microenvironment

Reversible transitions between noradrenergic and mesenchymal tumor identities define cell plasticity in neuroblastoma

Noradrenergic and mesenchymal identities have been characterized in neuroblastoma cell lines according to their epigenetic landscapes and core regulatory circuitries. However, their relationship and relative contribution in patient tumors remain poorly defined. We now document spontaneous and reversible plasticity between the two identities, associated with epigenetic reprogramming, in several neuroblastoma models. Interestingly, xenografts with cells from each identity eventually harbor a noradrenergic phenotype suggesting that the microenvironment provides a powerful pressure towards this phenotype. Accordingly, such a noradrenergic cell identity is systematically observed in single-cell RNA-seq of 18 tumor biopsies and 15 PDX models. Yet, a subpopulation of these noradrenergic tumor cells presents with mesenchymal features that are shared with plasticity models, indicating that the plasticity described in these models has relevance in neuroblastoma patients. This work therefore emphasizes that intrinsic plasticity properties of neuroblastoma cells are dependent upon external cues of the environment to drive cell identity

Evolutionary origins of Brassicaceae specific genes in Arabidopsis thaliana

<p>Abstract</p> <p>Background</p> <p>All sequenced genomes contain a proportion of lineage-specific genes, which exhibit no sequence similarity to any genes outside the lineage. Despite their prevalence, the origins and functions of most lineage-specific genes remain largely unknown. As more genomes are sequenced opportunities for understanding evolutionary origins and functions of lineage-specific genes are increasing.</p> <p>Results</p> <p>This study provides a comprehensive analysis of the origins of lineage-specific genes (LSGs) in <it>Arabidopsis thaliana </it>that are restricted to the Brassicaceae family. In this study, lineage-specific genes within the nuclear (1761 genes) and mitochondrial (28 genes) genomes are identified. The evolutionary origins of two thirds of the lineage-specific genes within the <it>Arabidopsis thaliana </it>genome are also identified. Almost a quarter of lineage-specific genes originate from non-lineage-specific paralogs, while the origins of ~10% of lineage-specific genes are partly derived from DNA exapted from transposable elements (twice the proportion observed for non-lineage-specific genes). Lineage-specific genes are also enriched in genes that have overlapping CDS, which is consistent with such novel genes arising from overprinting. Over half of the subset of the 958 lineage-specific genes found only in <it>Arabidopsis thaliana </it>have alignments to intergenic regions in <it>Arabidopsis lyrata</it>, consistent with either <it>de novo </it>origination or differential gene loss and retention, with both evolutionary scenarios explaining the lineage-specific status of these genes. A smaller number of lineage-specific genes with an incomplete open reading frame across different <it>Arabidopsis thaliana </it>accessions are further identified as accession-specific genes, most likely of recent origin in <it>Arabidopsis thaliana</it>. Putative <it>de novo </it>origination for two of the <it>Arabidopsis thaliana</it>-only genes is identified via additional sequencing across accessions of <it>Arabidopsis thaliana </it>and closely related sister species lineages. We demonstrate that lineage-specific genes have high tissue specificity and low expression levels across multiple tissues and developmental stages. Finally, stress responsiveness is identified as a distinct feature of Brassicaceae-specific genes; where these LSGs are enriched for genes responsive to a wide range of abiotic stresses.</p> <p>Conclusion</p> <p>Improving our understanding of the origins of lineage-specific genes is key to gaining insights regarding how novel genes can arise and acquire functionality in different lineages. This study comprehensively identifies all of the Brassicaceae-specific genes in <it>Arabidopsis thaliana </it>and identifies how the majority of such lineage-specific genes have arisen. The analysis allows the relative importance (and prevalence) of different evolutionary routes to the genesis of novel ORFs within lineages to be assessed. Insights regarding the functional roles of lineage-specific genes are further advanced through identification of enrichment for stress responsiveness in lineage-specific genes, highlighting their likely importance for environmental adaptation strategies.</p

Inférence statistique bayésienne pour les modélisations donnant lieu à un calcul de vraisemblance impossible

In a statistical inferential process, when the calculation of the likelihood function is not possible, approximations need to be used. This is a fairly common case in some application fields, especially for population genetics models. Toward this issue, we are interested in approximate Bayesian computation (ABC) methods. These are solely based on simulated data, which are then summarised and compared to the observed ones. The comparisons are performed depending on a distance, a similarity threshold and a set of low dimensional summary statistics, which must be carefully chosen.In a parameter inference framework, we propose an approach combining ABC simulations and the random forest machine learning algorithm. We use different strategies depending on the parameter posterior quantity we would like to approximate. Our proposal avoids the usual ABC difficulties in terms of tuning, while providing good results and interpretation tools for practitioners. In addition, we introduce posterior measures of error (i.e., conditionally on the observed data of interest) computed by means of forests. In a model choice setting, we present a strategy based on groups of models to determine, in population genetics, which events of an evolutionary scenario are more or less well identified. All these approaches are implemented in the R package abcrf. In addition, we investigate how to build local random forests, taking into account the observation to predict during their learning phase to improve the prediction accuracy. Finally, using our previous developments, we present two case studies dealing with the reconstruction of the evolutionary history of Pygmy populations, as well as of two subspecies of the desert locust Schistocerca gregaria.Dans un processus d’inférence statistique, lorsque le calcul de la fonction de vraisemblance associée aux données observées n’est pas possible, il est nécessaire de recourir à des approximations. C’est un cas que l’on rencontre très fréquemment dans certains champs d’application, notamment pour des modèles de génétique des populations. Face à cette difficulté, nous nous intéressons aux méthodes de calcul bayésien approché (ABC, Approximate Bayesian Computation) qui se basent uniquement sur la simulation de données, qui sont ensuite résumées et comparées aux données observées. Ces comparaisons nécessitent le choix judicieux d’une distance, d’un seuil de similarité et d’un ensemble de résumés statistiques pertinents et de faible dimension.Dans un contexte d’inférence de paramètres, nous proposons une approche mêlant des simulations ABC et les méthodes d’apprentissage automatique que sont les forêts aléatoires. Nous utilisons diverses stratégies pour approximer des quantités a posteriori d’intérêts sur les paramètres. Notre proposition permet d’éviter les problèmes de réglage liés à l’ABC, tout en fournissant de bons résultats ainsi que des outils d’interprétation pour les praticiens. Nous introduisons de plus des mesures d’erreurs de prédiction a posteriori (c’est-à-dire conditionnellement à la donnée observée d’intérêt) calculées grâce aux forêts. Pour des problèmes de choix de modèles, nous présentons une stratégie basée sur des groupements de modèles qui permet, en génétique des populations, de déterminer dans un scénario évolutif les évènements plus ou moins bien identifiés le constituant. Toutes ces approches sont implémentées dans la bibliothèque R abcrf. Par ailleurs, nous explorons des manières de construire des forêts aléatoires dites locales, qui prennent en compte l’observation à prédire lors de leur phase d’entraînement pour fournir une meilleure prédiction. Enfin, nous présentons deux études de cas ayant bénéficié de nos développements, portant sur la reconstruction de l’histoire évolutive de population pygmées, ainsi que de deux sous-espèces du criquet pèlerin Schistocerca gregaria

Le batelier Cupidon : chanson de genre / paroles de Louis Gleize ; musique de Louis Raynal ; créée par Debailleul à la Scala

Collection : Romances et chansons nouvelle